OPTIMIZED LINEAR COMBINATIONS OF CHANNELS FOR COMPLEX MULTIPLE-COIL B_1 FIELD ESTIMATION WITH BLOCH-SIEGERT B_1 MAPPING IN MRI

摘要

Bloch-Siegert B_1 mapping for multiple-channel parallel excitation systems usually produces noisy estimates in low intensity regions. Methods that use linear combinations of multiple coils have been proposed to mitigate this problem. However, little work has been done to optimize these coil combinations to improve the signal-to-noise ratio of B_1 mapping in a robust way. In this paper, we propose a Cramer-Rao Lower Bound analysis based method to optimize the coil combination matrix by minimizing the variance of B_1 map estimation for the previously proposed Bloch-Siegert B_1 mapping method. We illustrate how optimizing the coil combinations yields improved B_1 estimates in a simulation of brain imaging with a 3T MRI scan.